Automated modeling systems implement automated modeling algorithms (e.g., algorithms using modeling techniques such as logistic regression, neural networks, support vector machines, etc.) that are trained using large volumes of training data. This training data, which can be generated by or otherwise indicate certain electronic transactions or circumstances, is analyzed by one or more computing devices of an automated modeling system. The training data is grouped into predictor variables that are provided as inputs to the automated modeling system. The automated modeling system can use this analysis to learn from and make predictions using data describing similar circumstances. For example, the automated modeling system uses the predictor variables to learn how to generate predictive outputs involving transactions or other circumstances similar to the predictor variables from the training data.
One example of a model used by an automated modeling algorithm is a neural network model. A neural network includes one or more algorithms and interconnected nodes that share input data and exchange signals (e.g., communications of data inputs or interim processed data) between one another. The nodes can have numeric weights that can be tuned based on experience, which makes the neural network adaptive and capable of learning. For example, the numeric weights in the neural network can be trained so that the neural network can perform one or more functions on a set of inputs and produce an output that is associated with the set of inputs.